EXPOSURE MEASUREMENT ERRORS, RISK ESTIMATE AND STATISTICAL POWER IN CASE-CONTROL STUDIES USING DICHOTOMOUS-ANALYSIS OF A CONTINUOUS EXPOSURE VARIABLE

Background. Non-differential errors in exposure measurements have been shown to lead to differential misclassification of exposure. As a con sequence, the common tenet that, in absence of bias, imprecise exposur e assessment can only bias the risk estimates conservatively does not necessarily hold. We investigate the effects of exposure measurement e rrors on the risk estimate and on statistical power. Methods. We used a computer model that simulates a case-control study. We used both hyp othetical data and data modelled on empirical measurements of environm ental magnetic fields exposure. Results. Measurement errors are found to have a lesser impact on risk estimates and statistical power than w ould have been the case had misclassification been truly non-different ial. However, for a given cutpoint, a bias away from the null cannot b e excluded. The predominant direction of the errors is found to have i mportant consequences on both the study power and the risk estimates. Conclusion. When sufficient empirical data are available, computer mod elling may give a more accurate estimate of the effects of measurement errors than algebraic corrections.